A New Face Recognition Algorithm based on Dictionary Learning for a Single Training Sample per Person
نویسندگان
چکیده
The number of the training samples per person has a significant impact on face recognition (FR) performance. For the single training sample per person (STSPP) problem, most traditional FR algorithms exhibit performance degradation owing to the limited information available to predict the variance of the query sample. This paper proposes a new method for the STSPP problem in FR, namely the Learn-Generate-Classify (LGC) method. The overall framework of the LGC method is presented in Fig.1.
منابع مشابه
Sample diversity, representation effectiveness and robust dictionary learning for face recognition
Conventional dictionary learning algorithms suffer from the following problems when applied to face recognition. First, since in most face recognition applications there are only a limited number of original training samples, it is difficult to obtain a reliable dictionary with a large number of atoms from these samples. Second, because the face images of the same person vary with facial poses ...
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